cluster-analysis

$npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/cluster-analysis

Groups similar judgments into clusters and characterizes each

  • Solves the problem of identifying shared opinions in complex datasets
  • Uses judgment similarity analysis and clustering algorithms
  • Analyzes reasoning patterns to define cluster boundaries
  • Returns structured cluster data with summaries and counts

SKILL.md

.github/skills/cluster-analysisView on GitHub ↗
---
name: cluster-analysis
description: Identify natural opinion clusters from collected judgments and characterize each cluster.
execution: subagent
prompt: ./prompt.md
input: judgments[]
used-by: structured-consensus
---

# Cluster Analysis

Identify natural groupings of similar positions within the collected judgments. Characterize each cluster by its central position, shared reasoning patterns, and distinguishing features.

## Execution

Spawn a subagent that analyzes the judgments for similarity patterns, groups them into coherent clusters, and provides characterization of each cluster.

## Why Subagent

- Clustering requires holistic analysis of all judgments simultaneously
- Characterization is a bounded analytical task
- Output structure is standardized

## HARD-GATE

Output MUST contain: at least 2 clusters (if genuine disagreement exists), each with `cluster_id`, `position_summary`, `member_count`, and `characterization`. If all judgments agree, output 1 cluster with a note.

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